refactor input checks to init parameter variables

feature_engine/_check_init_parameters/check_variables.py

@@ -0,0 +1,39 @@
+from typing import Any, List, Union
+
+Variables = Union[None, int, str, List[Union[str, int]]]
+
+
+def _check_variables_input_value(variables: Variables) -> Any:
+    """
+    Checks that the input value for the `variables` parameter located in the init of
+    all Feature-engine transformers is of the correct type.
+    Allowed  values are None, int, str or list of strings and integers.
+
+    Parameters
+    ----------
+    variables : string, int, list of strings, list of integers. Default=None
+
+    Returns
+    -------
+    variables: same as input
+    """
+
+    msg = (
+        "`variables` should contain a string, an integer or a list of strings or "
+        f"integers. Got {variables} instead."
+    )
+    msg_dupes = "The list entered in `variables` contains duplicated variable names."
+    msg_empty = "The list of `variables` is empty."
+
+    if variables is not None:
+        if isinstance(variables, list):
+            if not all(isinstance(i, (str, int)) for i in variables):
+                raise ValueError(msg)
+            if len(variables) == 0:
+                raise ValueError(msg_empty)
+            if len(variables) != len(set(variables)):
+                raise ValueError(msg_dupes)
+        else:
+            if not isinstance(variables, (str, int)):
+                raise ValueError(msg)
+    return variables

feature_engine/_prediction/base_predictor.py

@@ -19,8 +19,8 @@
 )
 from feature_engine.encoding import MeanEncoder
 from feature_engine.tags import _return_tags
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling.variable_type_selection import (
     find_categorical_and_numerical_variables,
@@ -98,7 +98,7 @@ def __init__(
                 f"Got {strategy} instead."
             )
 
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.bins = bins
         self.strategy = strategy
 

feature_engine/creation/base_creation.py

@@ -5,7 +5,7 @@
 from sklearn.utils.validation import check_is_fitted
 
 from feature_engine._base_transformers.mixins import GetFeatureNamesOutMixin
-from feature_engine._check_input_parameters.check_init_input_params import (
+from feature_engine._check_init_parameters.check_init_input_params import (
     _check_param_drop_original,
     _check_param_missing_values,
 )

feature_engine/creation/cyclical_features.py

@@ -8,10 +8,13 @@
     FitFromDictMixin,
     GetFeatureNamesOutMixin,
 )
-from feature_engine._check_input_parameters.check_init_input_params import (
+from feature_engine._check_init_parameters.check_init_input_params import (
     _check_param_drop_original,
 )
-from feature_engine._check_input_parameters.check_input_dictionary import (
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
+)
+from feature_engine._check_init_parameters.check_input_dictionary import (
     _check_numerical_dict,
 )
 from feature_engine._docstrings.fit_attributes import (
@@ -28,9 +31,6 @@
     _transform_creation_docstring,
 )
 from feature_engine._docstrings.substitute import Substitution
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
-)
 
 
 @Substitution(
@@ -129,7 +129,7 @@ def __init__(
         _check_numerical_dict(max_values)
         _check_param_drop_original(drop_original)
 
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.max_values = max_values
         self.drop_original = drop_original
 

feature_engine/datetime/datetime.py

@@ -29,8 +29,8 @@
     FEATURES_SUFFIXES,
     FEATURES_SUPPORTED,
 )
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling._variable_type_checks import (
     _is_categorical_and_is_datetime,
@@ -219,7 +219,7 @@ def __init__(
         if utc is not None and not isinstance(utc, bool):
             raise ValueError("utc takes only booleans or None. " f"Got {utc} instead.")
 
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.drop_original = drop_original
         self.missing_values = missing_values
         self.dayfirst = dayfirst

feature_engine/datetime/datetime_subtraction.py

@@ -25,8 +25,8 @@
     check_X,
 )
 from feature_engine.variable_handling import find_or_check_datetime_variables
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 
 _example = """
@@ -198,8 +198,8 @@ def __init__(
                 )
 
         super().__init__(missing_values, drop_original)
-        self.variables = _check_init_parameter_variables(variables)
-        self.reference = _check_init_parameter_variables(reference)
+        self.variables = _check_variables_input_value(variables)
+        self.reference = _check_variables_input_value(reference)
         self.new_variables_names = new_variables_names
         self.output_unit = output_unit
         self.dayfirst = dayfirst

feature_engine/discretisation/decision_tree.py

@@ -20,8 +20,8 @@
 from feature_engine._docstrings.methods import _fit_transform_docstring
 from feature_engine._docstrings.substitute import Substitution
 from feature_engine.tags import _return_tags
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 
 
@@ -172,7 +172,7 @@ def __init__(
         self.cv = cv
         self.scoring = scoring
         self.regression = regression
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.param_grid = param_grid
         self.random_state = random_state
 

feature_engine/discretisation/equal_frequency.py

@@ -26,8 +26,8 @@
 )
 from feature_engine._docstrings.substitute import Substitution
 from feature_engine.discretisation.base_discretiser import BaseDiscretiser
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 
 
@@ -143,7 +143,7 @@ def __init__(
         super().__init__(return_object, return_boundaries, precision)
 
         self.q = q
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
 
     def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
         """

feature_engine/discretisation/equal_width.py

@@ -26,8 +26,8 @@
 )
 from feature_engine._docstrings.substitute import Substitution
 from feature_engine.discretisation.base_discretiser import BaseDiscretiser
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 
 
@@ -151,7 +151,7 @@ def __init__(
         super().__init__(return_object, return_boundaries, precision)
 
         self.bins = bins
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
 
     def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
         """

feature_engine/discretisation/geometric_width.py

@@ -23,8 +23,8 @@
     _transform_discretiser_docstring,
 )
 from feature_engine._docstrings.substitute import Substitution
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.discretisation.base_discretiser import BaseDiscretiser
 
@@ -42,7 +42,7 @@
     n_features_in_=_n_features_in_docstring,
     fit_transform=_fit_transform_docstring,
     power="{1/n}",
-    subindex="{i+1}"
+    subindex="{i+1}",
 )
 class GeometricWidthDiscretiser(BaseDiscretiser):
     """
@@ -142,7 +142,7 @@ def __init__(
         super().__init__(return_object, return_boundaries, precision)
 
         self.bins = bins
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
 
     def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
         """

feature_engine/encoding/base_encoder.py

@@ -18,8 +18,8 @@
     check_X,
 )
 from feature_engine.tags import _return_tags
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling.variable_type_selection import (
     find_all_variables,
@@ -54,7 +54,7 @@ def __init__(
                 f"Got {ignore_format} instead."
             )
 
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.ignore_format = ignore_format
 
 
@@ -95,7 +95,7 @@ def __init__(
                 f"Got {ignore_format} instead."
             )
 
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.ignore_format = ignore_format
         self.missing_values = missing_values
 

feature_engine/imputation/arbitrary_number.py

@@ -5,7 +5,7 @@
 
 import pandas as pd
 
-from feature_engine._check_input_parameters.check_input_dictionary import (
+from feature_engine._check_init_parameters.check_input_dictionary import (
     _check_numerical_dict,
 )
 from feature_engine._docstrings.fit_attributes import (
@@ -22,8 +22,8 @@
 from feature_engine._docstrings.substitute import Substitution
 from feature_engine.dataframe_checks import check_X
 from feature_engine.imputation.base_imputer import BaseImputer
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling.variable_type_selection import (
     find_or_check_numerical_variables,
@@ -125,7 +125,7 @@ def __init__(
 
         _check_numerical_dict(imputer_dict)
 
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
 
         self.imputer_dict = imputer_dict
 

feature_engine/imputation/categorical.py

@@ -19,8 +19,8 @@
 from feature_engine.dataframe_checks import check_X
 from feature_engine.imputation.base_imputer import BaseImputer
 from feature_engine.tags import _return_tags
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling.variable_type_selection import (
     find_all_variables,
@@ -144,7 +144,7 @@ def __init__(
 
         self.imputation_method = imputation_method
         self.fill_value = fill_value
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.return_object = return_object
         self.ignore_format = ignore_format
 

feature_engine/imputation/drop_missing_data.py

@@ -14,8 +14,8 @@
 from feature_engine.dataframe_checks import check_X
 from feature_engine.imputation.base_imputer import BaseImputer
 from feature_engine.tags import _return_tags
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling.variable_type_selection import find_all_variables
 
@@ -122,7 +122,7 @@ def __init__(
                     f"Got {threshold} instead."
                 )
 
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.missing_only = missing_only
         self.threshold = threshold
 

feature_engine/imputation/end_tail.py

@@ -21,8 +21,8 @@
 from feature_engine._docstrings.substitute import Substitution
 from feature_engine.dataframe_checks import check_X
 from feature_engine.imputation.base_imputer import BaseImputer
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling.variable_type_selection import (
     find_or_check_numerical_variables,
@@ -157,7 +157,7 @@ def __init__(
         self.imputation_method = imputation_method
         self.tail = tail
         self.fold = fold
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
 
     def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
         """

feature_engine/imputation/mean_median.py

@@ -21,8 +21,8 @@
 from feature_engine._docstrings.substitute import Substitution
 from feature_engine.dataframe_checks import check_X
 from feature_engine.imputation.base_imputer import BaseImputer
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling.variable_type_selection import (
     find_or_check_numerical_variables,
@@ -106,7 +106,7 @@ def __init__(
             raise ValueError("imputation_method takes only values 'median' or 'mean'")
 
         self.imputation_method = imputation_method
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
 
     def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
         """

feature_engine/imputation/missing_indicator.py

@@ -14,8 +14,8 @@
 from feature_engine.dataframe_checks import check_X
 from feature_engine.imputation.base_imputer import BaseImputer
 from feature_engine.tags import _return_tags
-from feature_engine.variable_handling._init_parameter_checks import (
-    _check_init_parameter_variables,
+from feature_engine._check_init_parameters.check_variables import (
+    _check_variables_input_value,
 )
 from feature_engine.variable_handling.variable_type_selection import find_all_variables
 
@@ -108,7 +108,7 @@ def __init__(
         if not isinstance(missing_only, bool):
             raise ValueError("missing_only takes values True or False")
 
-        self.variables = _check_init_parameter_variables(variables)
+        self.variables = _check_variables_input_value(variables)
         self.missing_only = missing_only
 
     def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):